ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library

ElegantRL: A Lightweight and Stable Deep Reinforcement Learning Library

In this article, we will learn how to implement deep reinforcement learning algorithms in 24 hours.

advantages of ElegantRL

One sentence summary of reinforcement learning (RL): in RL, an agent learns by continuously interacting with an environment, in a trial-and-error manner, making sequential decisions under uncertainty and achieving a balance between exploration (new territory) and exploitation (using knowledge learned from experiences).

Deep reinforcement learning (DRL) has great potential to solve real-world problems that are challenging to humans, such as self-driving cars, gaming, natural language processing (NLP), and financial trading. Starting from the success of AlphaGo, various DRL algorithms and applications are emerging in a disruptive manner. The ElegantRL library enables researchers and practitioners to pipeline the disruptive “design, development and deployment” of DRL technology.

The library to be presented is featured with “elegant” in the following aspects:

  • Lightweight: core codes have less than 1,000 lines, e.g., tutorial.
  • Efficient: the performance is comparable with Ray RLlib.
  • Stable: more stable than Stable Baseline 3.

ElegantRL supports state-of-the-art DRL algorithms, including discrete and continuous ones, and provides user-friendly tutorials in Jupyter notebooks.

The ElegantRL implements DRL algorithms under the Actor-Critic framework, where an Agent (a.k.a, a DRL algorithm) consists of an Actor network and a Critic network. Due to the completeness and simplicity of code structure, users are able to easily customize their own agents.

pytorch deep-learning openai-gym elegantrl

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